The training consists of two phases, the burn up stage and Teacher-Student Mutual Learning stage.
In the first stage, the Student is trained only on the labeled dataset, following the standard supervised training procedure.
Then, for the second stage, at each iteration, the Student is trained on two batches at the same time, one coming from the labeled dataset and the other coming from the unlabeled dataset.
For the latter, the baseline [1]} trains only the RPN and RoI head classifier and disregards regression.
The authors justify this choice noticing that the classification confidence thresholds are not able to filter the incorrect bounding box regression.
Our hypothesis is that training also on pseudo bounding box regression could help the Student as long as the task is correctly weighted with respect to the others.
Category classification and regression tasks are learned in parallel.
Given that the classification performance improves during the training, we can also expect the bounding box regression to behave the same way.
In other words, while the training proceeds, the average quality of the pseudo-labels increases and so does the classification confidence value.
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